Soft measurement method for key parameters in edible fungus fermentation production process

Abstract

The invention discloses a soft measurement method for the key parameters in the edible fungus fermentation production process, and the method is used for solving the problem of online estimation of the key biochemical quantity which is difficult to measure in real time in the edible fungus fermentation process. The method comprises the following steps: firstly, by analyzing the process mechanism of an edible fungus fermentation process, selecting a proper auxiliary variable and establishing a training sample database according to the historical tank batch data; constructing a least squares support vector machine soft measurement training sample database by combining the main variables and the auxiliary variables of the current to-be-predicted tank batch fermentation process with the main variables and the historical auxiliary variables of the historical fermentation process, and constructing a soft measurement model corresponding to the soft measurement training sample database, and then optimizing the normalization parameter gamma and the kernel parameter sigma 2 in the soft measurement model by means of a grey wolf algorithm, and establishing a least squares support vector machine soft measurement model based on the grey-wolf optimization, and finally obtaining a corresponding key biochemical parameter prediction value. According to the method, the grey-wolf optimization algorithm for simulating the grey wolf behaviors is adopted, the structure is simple, the parameter setting is small, the global searching capability is high, and the gradient information is not considered.

Description

technical field [0001] The invention belongs to the technical field of soft measurement and soft instrument structure, and specifically relates to a method for solving the three key biochemical problems of bacteria concentration, substrate concentration and edible fungus product quality that are difficult to measure online and real-time with physical sensors during the fermentation production process of edible fungi. On-line Estimation Problems of Variables. Background technique [0002] With the rapid development of the edible fungi industry, my country's traditional small-scale lagging production mode can no longer meet the requirements of the current market. Therefore, the realization of edible fungus liquid submerged fermentation and automatic production will be the mainstream planting mode in my country in the future. However, the first problem encountered in the application of advanced automated production is that the quality of edible mushroom fruiting bodies and othe...

AI Technical Summary

Problems solved by technology

When establishing the LS-SVM soft sensor model of the fermentation process of edible fungi, the regularization parameter γ and the kernel parameter σ 2 directly affects the fitting performance and generalization ability of the soft sensor model, but in actual use, the regularization parameter γ and kernel parameter σ of the LS-SVM soft sensor model 2 The probability of falling into a local extremum is high, and the global optimization ability is poor, which affects its measurement accuracy

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